from os import listdir, path
import numpy as np
import scipy, cv2, os, sys, argparse, audio
import json, subprocess, random, string
from tqdm import tqdm
import platform
from PIL import Image

import onnxruntime
onnxruntime.set_default_logger_severity(3)
from insightface_func.face_detect_crop_single import Face_detect_crop

parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models')

parser.add_argument('--checkpoint_path', type=str, help='Name of saved checkpoint to load weights from', required=True)

parser.add_argument('--face', type=str, help='Filepath of video/image that contains faces to use', required=True)

parser.add_argument('--audio', type=str, help='Filepath of video/audio file to use as raw audio source', required=True)

parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.', default='results/result_voice.mp4')

parser.add_argument('--static', type=bool, help='If True, then use only first video frame for inference', default=False)

parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)', default=25., required=False)

parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0], help='Padding (top, bottom, left, right). Please adjust to include chin at least')

parser.add_argument('--face_det_batch_size', type=int, help='Batch size for face detection', default=16)

parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=1)

parser.add_argument('--resize_factor', default=1, type=int, help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p')

parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1], help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. ' 'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width')

parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1], help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.''Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).')

parser.add_argument('--rotate', default=False, action='store_true',help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.''Use if you get a flipped result, despite feeding a normal looking video')

parser.add_argument('--nosmooth', default=False, action='store_true',help='Prevent smoothing face detections over a short temporal window')

#parser.add_argument('--img_size', type=int, help='wav2lip model input size (96 or 256)', default=96., required=False)

args = parser.parse_args()


if args.checkpoint_path.endswith('wav2lip_256.onnx') or args.checkpoint_path.endswith('wav2lip_256_fp16.onnx'):
	args.img_size = 256
else:
	args.img_size = 96


if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
	args.static = True

	
def load_model(device):
	model_path = args.checkpoint_path
	session_options = onnxruntime.SessionOptions()
	session_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
	providers = ["CPUExecutionProvider"]
	if device == 'cuda':
		providers = [("CUDAExecutionProvider", {"cudnn_conv_algo_search": "DEFAULT"}),"CPUExecutionProvider"]
	session = onnxruntime.InferenceSession(model_path, sess_options=session_options, providers=providers)
	#self.resolution = self.session.get_inputs()[0].shape[-2:]	
	
	return session
		
def get_smoothened_boxes(boxes, T):
	for i in range(len(boxes)):
		if i + T > len(boxes):
			window = boxes[len(boxes) - T:]
		else:
			window = boxes[i : i + T]
		boxes[i] = np.mean(window, axis=0)
	return boxes

def face_detect(images):

	detector = Face_detect_crop(name='antelope', root='./insightface_func/models')
	detector.prepare(ctx_id= 0, det_thresh=0.3, det_size=(320,320),mode='none')

	predictions = []
	for i in tqdm(range(0, len(images))):
		bbox = detector.getBox(images[i]) 
		predictions.append(bbox)
	
	results = []
	pady1, pady2, padx1, padx2 = args.pads
	for rect, image in zip(predictions, images):
		if rect is None:
			cv2.imwrite('temp/faulty_frame.jpg', image) # check this frame where the face was not detected.
			raise ValueError('Face not detected! Ensure the video contains a face in all the frames.')

		y1 = max(0, rect[1] - pady1)
		y2 = min(image.shape[0], rect[3] + pady2)
		x1 = max(0, rect[0] - padx1)
		x2 = min(image.shape[1], rect[2] + padx2)
		
		results.append([x1, y1, x2, y2])

	boxes = np.array(results)
	if not args.nosmooth: boxes = get_smoothened_boxes(boxes, T=5)
	results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)]
		
	del detector
	return results 

def datagen(frames, mels):
	
	img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

	if args.box[0] == -1:
		if not args.static:
			face_det_results = face_detect(frames) # BGR2RGB for CNN face detection
		else:
			face_det_results = face_detect([frames[0]])
	else:
		print('Using the specified bounding box instead of face detection...')
		y1, y2, x1, x2 = args.box
		face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames]

	for i, m in enumerate(mels):
		idx = 0 if args.static else i%len(frames)
		frame_to_save = frames[idx].copy()
		face, coords = face_det_results[idx].copy()

		face = cv2.resize(face, (args.img_size, args.img_size))
			
		img_batch.append(face)
		mel_batch.append(m)
		frame_batch.append(frame_to_save)
		coords_batch.append(coords)

		if len(img_batch) >= args.wav2lip_batch_size:
			img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)

			img_masked = img_batch.copy()
			img_masked[:, args.img_size//2:] = 0

			img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
			mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])

			yield img_batch, mel_batch, frame_batch, coords_batch
			img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

	if len(img_batch) > 0:
		img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)

		img_masked = img_batch.copy()
		img_masked[:, args.img_size//2:] = 0

		img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
		mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])

		yield img_batch, mel_batch, frame_batch, coords_batch

mel_step_size = 16
device = 'cuda'
#device = 'cuda' if torch.cuda.is_available() else 'cpu' only if torch is installed

def to_numpy(tensor):
	return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
    
def main():
	im = cv2.imread(args.face)
	#input(im.shape)
	if not os.path.isfile(args.face):
		raise ValueError('--face argument must be a valid path to video/image file')
	
	elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
		full_frames = [cv2.imread(args.face)]
		fps = args.fps

	else:
		video_stream = cv2.VideoCapture(args.face)
		fps = video_stream.get(cv2.CAP_PROP_FPS)

		print('Reading video frames...')

		full_frames = []
		while 1:
			still_reading, frame = video_stream.read()
			if not still_reading:
				video_stream.release()
				break
			if args.resize_factor > 1:
				frame = cv2.resize(frame, (frame.shape[1]//args.resize_factor, frame.shape[0]//args.resize_factor))

			if args.rotate:
				frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)

			y1, y2, x1, x2 = args.crop
			if x2 == -1: x2 = frame.shape[1]
			if y2 == -1: y2 = frame.shape[0]

			frame = frame[y1:y2, x1:x2]

			full_frames.append(frame)

	print ("Number of frames available for inference: "+str(len(full_frames)))

	if not args.audio.endswith('.wav'):
		print('Extracting raw audio...')
		command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav')

		subprocess.call(command, shell=True)
		args.audio = 'temp/temp.wav'

	wav = audio.load_wav(args.audio, 16000)
	mel = audio.melspectrogram(wav)
	#print(mel.shape)

	if np.isnan(mel.reshape(-1)).sum() > 0:
		raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')

	mel_chunks = []
	mel_idx_multiplier = 80./fps 
	i = 0
	while 1:
		start_idx = int(i * mel_idx_multiplier)
		if start_idx + mel_step_size > len(mel[0]):
			mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
			break
		mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
		i += 1

	print("Length of mel chunks: {}".format(len(mel_chunks)))

	full_frames = full_frames[:len(mel_chunks)]

	batch_size = 1
	gen = datagen(full_frames.copy(), mel_chunks)

	for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, 
											total=int(np.ceil(float(len(mel_chunks))/batch_size)))):
		if i == 0:

			model = load_model(device) # load wav2lip.onnx 

			frame_h, frame_w = full_frames[0].shape[:-1]
			out = cv2.VideoWriter('temp/result.avi', cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))
		
		img_batch = img_batch.transpose((0, 3, 1, 2)).astype(np.float32)
		mel_batch = mel_batch.transpose((0, 3, 1, 2)).astype(np.float32)		

		pred = model.run(None,{'mel_spectrogram':mel_batch, 'video_frames':img_batch})[0][0]
		pred = pred.transpose(1, 2, 0)*255
		pred = pred.astype(np.uint8)
		#cv2.imshow("P",pred)
		pred = pred.reshape((1, args.img_size, args.img_size, 3))		

		for p, f, c in zip(pred, frames, coords):

			y1, y2, x1, x2 = c
			p = cv2.resize(p, (x2 - x1, y2 - y1))
			#cv2.imshow("pred",p)
			f[y1:y2, x1:x2] = p
			out.write(f)
			cv2.imshow("Result",f)
			cv2.waitKey(1)
			
	out.release()

	command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/result.avi', args.outfile)
	subprocess.call(command, shell=platform.system() != 'Windows')
	os.remove('temp/result.avi')

if __name__ == '__main__':
	main()
